ADHD Dataset

This dataset is composed of real patient responses to two questionnaires related to ADHD and Mood Disorder and a variety of demographic, abuse and drug use variarables. For each questionnaire, the responses to individual questions are provided along with total scores. Links to the actual questions are provided below:

The first part of this work will make use of unsupervised learning techniques such as Principal Component Analysis (PCA) and clustering in an attempt to discover structures in the data. The second part will explore support vector machines in a supervised learning exercise to predict whether an individual has attempted suicide.

Data

The dataset is composed of 54 variables and 175 observations. The data is coded as numeric and holds 33 observations that have some level of missing data. A summary of the variable distributions is provided below:

Data summary
Name adhd %>% select(-c(ADHD.Q…
Number of rows 175
Number of columns 21
_______________________
Column type frequency:
factor 1
numeric 20
________________________
Group variables None

Variable type: factor

skim_variable n_missing complete_rate ordered n_unique top_counts
Initial 0 1 FALSE 109 DB: 5, CM: 4, DJ: 4, JM: 4

Variable type: numeric

skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist
Age 0 1.00 39.47 11.17 18 29.5 42 48.0 69 ▆▅▇▅▁
Sex 0 1.00 1.43 0.50 1 1.0 1 2.0 2 ▇▁▁▁▆
Race 0 1.00 1.64 0.69 1 1.0 2 2.0 6 ▇▁▁▁▁
ADHD.Total 0 1.00 34.32 16.70 0 21.0 33 47.5 72 ▃▆▇▆▂
MD.TOTAL 0 1.00 10.02 4.81 0 6.5 11 14.0 17 ▃▃▆▇▇
Alcohol 4 0.98 1.35 1.39 0 0.0 1 3.0 3 ▇▂▁▁▆
THC 4 0.98 0.81 1.27 0 0.0 0 1.5 3 ▇▁▁▁▃
Cocaine 4 0.98 1.09 1.39 0 0.0 0 3.0 3 ▇▁▁▁▅
Stimulants 4 0.98 0.12 0.53 0 0.0 0 0.0 3 ▇▁▁▁▁
Sedative.hypnotics 4 0.98 0.12 0.54 0 0.0 0 0.0 3 ▇▁▁▁▁
Opioids 4 0.98 0.39 0.99 0 0.0 0 0.0 3 ▇▁▁▁▁
Court.order 5 0.97 0.09 0.28 0 0.0 0 0.0 1 ▇▁▁▁▁
Education 9 0.95 11.90 2.17 6 11.0 12 13.0 19 ▁▅▇▂▁
Hx.of.Violence 11 0.94 0.24 0.43 0 0.0 0 0.0 1 ▇▁▁▁▂
Disorderly.Conduct 11 0.94 0.73 0.45 0 0.0 1 1.0 1 ▃▁▁▁▇
Suicide 13 0.93 0.30 0.46 0 0.0 0 1.0 1 ▇▁▁▁▃
Abuse 14 0.92 1.33 2.12 0 0.0 0 2.0 7 ▇▂▁▁▁
Non.subst.Dx 22 0.87 0.44 0.68 0 0.0 0 1.0 2 ▇▁▃▁▁
Subst.Dx 23 0.87 1.14 0.93 0 0.0 1 2.0 3 ▆▇▁▅▂
Psych.meds. 118 0.33 0.96 0.80 0 0.0 1 2.0 2 ▇▁▇▁▆

Data Processing

The dataset is modified to include an EducationLevel categorical variable derived from the numerical Education variables representing the years of schooling. The Abuse column is unfolded into 3 binary variables indicating the occurence of the 3 types of abuse. The original Abuse variable is dropped.

We work with a multiple subsets of the data for subsequent parts this report. Some analyses make use of the entire set of questionnaire reponses while others use only the total score.

Missing Values

The dataset contains a few missing values. The PsychMeds variable mostly contained missing values and was dropped entirely. A few observations were quite sparse and only contained basic demographic and questionnaire score columns. In order to avoid biasing the dataset with imputed values, we preferred to drop all observations with missing values from the dataset. The resulting dataset contains 33 fewer observations with 142 complete rows and 19 columns.

## 
##  Variables sorted by number of missings: 
##        Variable      Count
##       PsychMeds 0.67428571
##         SubstDX 0.13142857
##      NonSubstDX 0.12571429
##          AbuseP 0.08000000
##          AbuseS 0.08000000
##          AbuseE 0.08000000
##         Suicide 0.07428571
##        Violence 0.06285714
##         Conduct 0.06285714
##       Education 0.05142857
##  EducationLevel 0.05142857
##      CourtOrder 0.02857143
##         Alcohol 0.02285714
##             THC 0.02285714
##         Cocaine 0.02285714
##      Stimulants 0.02285714
##       Sedatives 0.02285714
##         Opioids 0.02285714
##             Age 0.00000000
##             Sex 0.00000000
##            Race 0.00000000
##       ADHDTotal 0.00000000
##         MDTotal 0.00000000

Data Exploration

Clustering

Clustering refers to a broad set of techniques for finding subgroups, or clusters, in a dataset. We seek to partition observations into distinct groups so that the observations within each group are quite similar to each other, while observations in different groups are quite different from each other. The most popular clustering approaches are K-means and Hierarchical Clustering (HC). While the former requires a pre-specified number of clusters k, the latter does not. HC is a bottom-up or agglomerative clustering approach which results in an upside-down tree representation, built from the leaves and combined into clusters up to the trunk. Clusters are identified by horizontal cuts across the dendrogram.

In this section, we explore the use of Hierarchical Clustering on two portions of the data. The first uses only the questionnaire responses to ADHD while the second uses the total questionnaire scores for both surveys as well as the other variables (demographic, drugs, abuse, etc). The latter is referred to as the ‘reduced’ dataset.

Clustering typically requires the variables to be scaled in order to avoid more weight to variables using a larger range of values. However, when all the variables under conideration are measured on the same scale, which is the case when only comparing survey responses, it can be appropriate to leave the variables unscaled.

With HC, the concept of dissimilarity between a pair of observations needs to be extended to a pair of groups of observations. This extension is achieved with the notion of linkage, which defines the dissimilarity between two groups of observations. The resulting dendrogram heavily depends on the choice of linkage. The most popular linkages are complete and average because they tend to result in more balanced clusters.

ADHD Questionnaire

Using only the individual unscaled responses to the ADHD Questionnaire, we obtain the following dendrogram structure using complete linkage. In this case, complete linkage provided the best balancing and a cutoff into 3 clusters looked appropriate. In order to gain insight into these clusters, we need to look at the distribution of the variables within each of them.

For these 3 clusters, we can make the following observations:

  • The clusters have very similar average age.
  • The consistuents of cluster 3 have the lowest average ADHD and Mood Disorder total scores.
  • Cluster 1 in on the other end of the spectrum and has the largest average lowest average ADHD and Mood Disorder total scores.
  • Clusters 2 is the largest subgroup with scores falling in between clusters 1 and 3

We can establish a ranking for this clustering based on the monotic rise in the meanADHD and meanMD across clusters. From less to most severe: Cluster 3, Cluster 2, Cluster 1. With this information, a treatment plan could be developped to focus on clusters 2 and 1, with the latter requiring special attention.

Reduced Dataset

For a contrasting analysis, we looked at clustering based on the dataset which included only the total questionnaire scores and dropped observations with missing values. The variables were scaled to balance out the contribution of the high values for scores and age. We explore the use of complete, average and Ward linkages.

Complete Linkage

Unlike the previous clustering, adding the other variables results in a less balanced dendrogram. In this case, there are a few clusters containing only a few observations and two large clusters containing the majority of the data. We can make a few additional observations:

  • The largest cluster has the highest meanAge but the lowest average ADHD and MD scores. The value for meanADHD in this group is about twice the value of the least severe group in the previous cluster (ADHD only).
  • Clusters 5 and 6 are the smallest and youngest grouping on average. Interestly, the individuals making up these groups are on the highest end of the meanADHD and meanMD variables. These individual may consitute a subgroup for potential treatment.
  • Clusters 1 and 3 are have very close meanMD values but remain dissimilar in terms of age and meanADHD scores. Cluster 3 is on average older by about 6 years but Cluster 1 has nearly 10 more ADHD points.

Based on the variable distributions, it might be tempting to want to group clusters 4, 5 and 6 together since they represent a younger cohort with similar meanMD and meanADHD scores. However, hierachichal clustering considers more variables than the aforementioned few and this kind of grouping would violate the measure of similarity as determined by the y axis of the dendrogram.

A proposed ADHD and Mood Disorder ranking in 4 levels from least severe to most severe is: Cluster 2, Clusters 3 (younger) & 4 (older), Clusters 5 (younger) & 1 (older), Cluster 6.

Average Linkage

Average linkage completely changes the dendrogram representation. The same number of clusters, k=6 seemed appropriate. Similarly to complete linkage, one large cluster is identified, this time containing the vast majority of the observations. This obscures the analysis beyond what we can establish about the small clusters.

  • Clusters 6 and Cluster 5, both of size 1, represented the most severe and less severe cases respectively. It is interesting to note that Cluster 5 in more similar to the rest of the data in term of hierachy than to Cluster 6.
  • Cluster 4 contains a handful of individuals that might also be considered severe.
  • The large Cluster 2 must contain a variety of individuals that may have large variations in ADHD and MD scores but are nevertheless similar based on other attributes.

Ward Linkage

While average linkage is a popular option, the resulting clustering above was somewhat unattractive. Here we consider another linkage method to expand the analysis. Ward linkage makes use of Ward’s minimum variance criterion whcih seeks to minimizes the total within-cluster variance. The resulting dendrogram is visually attractive and more balanced than before. A cutoff at k=5 clusters seems appropriate. While more balanced, the insights drawn from the variable distributions across the clusters are not as obvious as when using complete linkage. It is harder to determine the least and most severe clusters as the grouping with the lower meanMD score also has the highest meanADHD score. Clusters 1 and 3 have similar questionnaire scores while differing by more than 10 years on average.

Comparison

We use a tanglegram to obtain side-by-side comparisons on the clusters obtained using the different linkage methods. Here we only compare the complete and average linkage clusterings which are similar in terms of cluster imbalance. The first thing to notice is the consistency of groupings at the higher ends of the hiearchies. This is shown by the horizontal ribbon linking the two clustering and indicates that the selected observations end up in the corresponsing cluster representation. Naturally, a large number of observations find correspondence in the opposing largest clusters. A few observations cross the tanglegram to the opposite corner from smaller clusters. Further study of these patients might be of interest.

[MI: comment or delete]

Principal Component Analysis

Principal Comnponent Analysis (PCA) is a dimensionality reduction technique where a dataset is transformed to use p eigenvectors of the covariance matrix instead of the original number of predictors n, where p < n. The number of eigenvectors p is selected by looking at the sorted eigenvalues and determining a threshold percentage of variance explained and the resulting p.

The method seeks to project the data into a lower dimensional space where each axis (or principal component) captures the most variability in the data subject to the condition of being uncorrelated to the other axes. This last condition is important for dimensionality reduction in the sense that large datasets can contain many correlated variables which hold no additional information.

An eigenvalue > 1 indicates that PCs account for more variance than accounted by one of the original variables in standardized data. This is commonly used as a cutoff point for which PCs are retained. This holds true only when the data are standardized. We can also limit the number of component to that number that accounts for a certain fraction of the total variance, for example 70%.

ADHD Questionnaire

This section focuses on the subset of the data containing only the individual responses to the ADHD questionnaire. The table below displays the first 10 eigenvalues obtained from the decomposition. Following from the cutoff decription above, our selection of dimensions can be based on the number of scaled eigenvalues that are greater than 1 or on a certain percentage of cummulative variance explained. Another way to select the number of PCs to consider is to study the scree plot provided below, which is simply a visual representation of the variance explained by each component. We typically look for an elbow in the plot to make our selection. In our case, the scree plot elbow occurs at Dimensions = 2 and the eigenvalue threshold at Dimensions = 3 which account for 59.2% and 64.8% respectively.

We can study the individual contributions of each questionnaire response to the principal components using the plot below. We observe that the contributions to the first principal component (Dim. 1) by the variables is roughly equivalent. However, Q16 bears a lot of weight in the 2nd dimension as Q5 does in the third dimension.

The cummulative contribution across the first 3 dimensions is summarized below. Q5 bears the most overall importance, followed by Q16 and Q4. It is worth diving into the actual questionnaire to look up what each question is asking to see if any insights can be drawn to explain the variability. The questions are listed below.

  • Q4: When you have a task that requires a lot of thought, how often do you avoid or delay getting started?
  • Q5: How often do you fidget or squirm with your hands or feet when you have to sit down for a long time?
  • Q16: When you’re in a conversation, how often do you find yourself finishing the sentences of the people you are talking to, before they can finish them themselves?

We can visualize these contribution in 2 dimensions using the first two PCs as shown below. On this plot, we look at the groupings and directions of the vectors. Positively correlated variables are grouped together. Negatively correlated variables are positioned on opposite sides of the plot origin. The distance between variables and the origin measures the contribution of the variables. We make the following observations:

  • Q16 is most red and shown as the most contributing variable in this representation due to it’s importance in PC2 which we saw above.
  • Q15, Q17 and Q18 are closely correlated while being away from the correlated vectors near the PC1 axis.
  • Most vectors hug the PC1 axis as can be explained from the fairly even contributions to PC1 reveale don the correlation plot.
  • Q16, A9, Q4 and Q5 have the largest distances from the origin and contribute similarly to PC1 but contribute quite differently to PC2 where Q16 dominates

Reduced Dataset

Using the resduced dataset, we can use PCA to extract additional information about the patients. Since we are using total scores for this section, the variables must be scaled in order to avoid any overweighted contributions. We find that it takes over 8 dimensions to explain approximately 70% of the variance. The scree plot has no obvious elbow which can be used to determine a cutoff. Using the eigenvalue cutoff at Dimension 7, we can still capture 65% of the variance.

From the correlation and variable contribution plots below, we can make a few observations:

  • SubstDX is the largest contributor to PC1 by far
  • ADHDTotal is the most important contribution to PC2
  • Sedatives and Opiods contribute about equally to PC3
  • Demographic factors Age, Race and Sex are the greatest contributors to PC4, 5 & 6 respectfully.
  • Stimulants is the leading contribution to PC7 which is our cutoff.

Cummulatively for PC1 to PC7, we can observe from the below that SubstDX is the largest individual contributor to the principal components, followed by Age, Race and the two ADHD and Mood Disorder questionnaires.

The 2D representation of the variable contributions to PC1 and PC2 are shown below. SubstDX has the largest contribution in the direction of the PC2 axis. In the same direction we can identify lesser but nevertheless present contributions from variables such as Violence, Conduct, Cocaine, THC and Alcohol.

[MI: add comments]

SVM

SVM - Via Dataset

## 
## Parameter tuning of 'svm':
## 
## - sampling method: 10-fold cross validation 
## 
## - best parameters:
##  kernel cost epsilon
##  radial    4       0
## 
## - best performance: 0.2727273 
## 
## - Detailed performance results:
##         kernel cost epsilon     error dispersion
## 1       linear    4     0.0 0.3325758 0.15719367
## 2   polynomial    4     0.0 0.3068182 0.08035304
## 3       radial    4     0.0 0.2727273 0.13184624
## 4      sigmoid    4     0.0 0.3522727 0.14142541
## 5       linear    8     0.0 0.3318182 0.15475493
## 6   polynomial    8     0.0 0.3159091 0.09181993
## 7       radial    8     0.0 0.2977273 0.11736585
## 8      sigmoid    8     0.0 0.3772727 0.15569979
## 9       linear   16     0.0 0.3318182 0.15475493
## 10  polynomial   16     0.0 0.3068182 0.12768142
## 11      radial   16     0.0 0.3333333 0.11741745
## 12     sigmoid   16     0.0 0.3765152 0.14436186
## 13      linear   32     0.0 0.3318182 0.15475493
## 14  polynomial   32     0.0 0.3333333 0.12761898
## 15      radial   32     0.0 0.2984848 0.15419350
## 16     sigmoid   32     0.0 0.3689394 0.17784843
## 17      linear   64     0.0 0.3318182 0.15475493
## 18  polynomial   64     0.0 0.3250000 0.09964033
## 19      radial   64     0.0 0.2984848 0.14712495
## 20     sigmoid   64     0.0 0.3924242 0.13047518
## 21      linear  128     0.0 0.3318182 0.15475493
## 22  polynomial  128     0.0 0.3250000 0.09964033
## 23      radial  128     0.0 0.3166667 0.14225938
## 24     sigmoid  128     0.0 0.3333333 0.11065102
## 25      linear  256     0.0 0.3318182 0.15475493
## 26  polynomial  256     0.0 0.3250000 0.09964033
## 27      radial  256     0.0 0.3166667 0.14225938
## 28     sigmoid  256     0.0 0.3954545 0.11609572
## 29      linear    4     0.1 0.3325758 0.15719367
## 30  polynomial    4     0.1 0.3068182 0.08035304
## 31      radial    4     0.1 0.2727273 0.13184624
## 32     sigmoid    4     0.1 0.3522727 0.14142541
## 33      linear    8     0.1 0.3318182 0.15475493
## 34  polynomial    8     0.1 0.3159091 0.09181993
## 35      radial    8     0.1 0.2977273 0.11736585
## 36     sigmoid    8     0.1 0.3772727 0.15569979
## 37      linear   16     0.1 0.3318182 0.15475493
## 38  polynomial   16     0.1 0.3068182 0.12768142
## 39      radial   16     0.1 0.3333333 0.11741745
## 40     sigmoid   16     0.1 0.3765152 0.14436186
## 41      linear   32     0.1 0.3318182 0.15475493
## 42  polynomial   32     0.1 0.3333333 0.12761898
## 43      radial   32     0.1 0.2984848 0.15419350
## 44     sigmoid   32     0.1 0.3689394 0.17784843
## 45      linear   64     0.1 0.3318182 0.15475493
## 46  polynomial   64     0.1 0.3250000 0.09964033
## 47      radial   64     0.1 0.2984848 0.14712495
## 48     sigmoid   64     0.1 0.3924242 0.13047518
## 49      linear  128     0.1 0.3318182 0.15475493
## 50  polynomial  128     0.1 0.3250000 0.09964033
## 51      radial  128     0.1 0.3166667 0.14225938
## 52     sigmoid  128     0.1 0.3333333 0.11065102
## 53      linear  256     0.1 0.3318182 0.15475493
## 54  polynomial  256     0.1 0.3250000 0.09964033
## 55      radial  256     0.1 0.3166667 0.14225938
## 56     sigmoid  256     0.1 0.3954545 0.11609572
## 57      linear    4     0.2 0.3325758 0.15719367
## 58  polynomial    4     0.2 0.3068182 0.08035304
## 59      radial    4     0.2 0.2727273 0.13184624
## 60     sigmoid    4     0.2 0.3522727 0.14142541
## 61      linear    8     0.2 0.3318182 0.15475493
## 62  polynomial    8     0.2 0.3159091 0.09181993
## 63      radial    8     0.2 0.2977273 0.11736585
## 64     sigmoid    8     0.2 0.3772727 0.15569979
## 65      linear   16     0.2 0.3318182 0.15475493
## 66  polynomial   16     0.2 0.3068182 0.12768142
## 67      radial   16     0.2 0.3333333 0.11741745
## 68     sigmoid   16     0.2 0.3765152 0.14436186
## 69      linear   32     0.2 0.3318182 0.15475493
## 70  polynomial   32     0.2 0.3333333 0.12761898
## 71      radial   32     0.2 0.2984848 0.15419350
## 72     sigmoid   32     0.2 0.3689394 0.17784843
## 73      linear   64     0.2 0.3318182 0.15475493
## 74  polynomial   64     0.2 0.3250000 0.09964033
## 75      radial   64     0.2 0.2984848 0.14712495
## 76     sigmoid   64     0.2 0.3924242 0.13047518
## 77      linear  128     0.2 0.3318182 0.15475493
## 78  polynomial  128     0.2 0.3250000 0.09964033
## 79      radial  128     0.2 0.3166667 0.14225938
## 80     sigmoid  128     0.2 0.3333333 0.11065102
## 81      linear  256     0.2 0.3318182 0.15475493
## 82  polynomial  256     0.2 0.3250000 0.09964033
## 83      radial  256     0.2 0.3166667 0.14225938
## 84     sigmoid  256     0.2 0.3954545 0.11609572
## 85      linear    4     0.3 0.3325758 0.15719367
## 86  polynomial    4     0.3 0.3068182 0.08035304
## 87      radial    4     0.3 0.2727273 0.13184624
## 88     sigmoid    4     0.3 0.3522727 0.14142541
## 89      linear    8     0.3 0.3318182 0.15475493
## 90  polynomial    8     0.3 0.3159091 0.09181993
## 91      radial    8     0.3 0.2977273 0.11736585
## 92     sigmoid    8     0.3 0.3772727 0.15569979
## 93      linear   16     0.3 0.3318182 0.15475493
## 94  polynomial   16     0.3 0.3068182 0.12768142
## 95      radial   16     0.3 0.3333333 0.11741745
## 96     sigmoid   16     0.3 0.3765152 0.14436186
## 97      linear   32     0.3 0.3318182 0.15475493
## 98  polynomial   32     0.3 0.3333333 0.12761898
## 99      radial   32     0.3 0.2984848 0.15419350
## 100    sigmoid   32     0.3 0.3689394 0.17784843
## 101     linear   64     0.3 0.3318182 0.15475493
## 102 polynomial   64     0.3 0.3250000 0.09964033
## 103     radial   64     0.3 0.2984848 0.14712495
## 104    sigmoid   64     0.3 0.3924242 0.13047518
## 105     linear  128     0.3 0.3318182 0.15475493
## 106 polynomial  128     0.3 0.3250000 0.09964033
## 107     radial  128     0.3 0.3166667 0.14225938
## 108    sigmoid  128     0.3 0.3333333 0.11065102
## 109     linear  256     0.3 0.3318182 0.15475493
## 110 polynomial  256     0.3 0.3250000 0.09964033
## 111     radial  256     0.3 0.3166667 0.14225938
## 112    sigmoid  256     0.3 0.3954545 0.11609572
## 113     linear    4     0.4 0.3325758 0.15719367
## 114 polynomial    4     0.4 0.3068182 0.08035304
## 115     radial    4     0.4 0.2727273 0.13184624
## 116    sigmoid    4     0.4 0.3522727 0.14142541
## 117     linear    8     0.4 0.3318182 0.15475493
## 118 polynomial    8     0.4 0.3159091 0.09181993
## 119     radial    8     0.4 0.2977273 0.11736585
## 120    sigmoid    8     0.4 0.3772727 0.15569979
## 121     linear   16     0.4 0.3318182 0.15475493
## 122 polynomial   16     0.4 0.3068182 0.12768142
## 123     radial   16     0.4 0.3333333 0.11741745
## 124    sigmoid   16     0.4 0.3765152 0.14436186
## 125     linear   32     0.4 0.3318182 0.15475493
## 126 polynomial   32     0.4 0.3333333 0.12761898
## 127     radial   32     0.4 0.2984848 0.15419350
## 128    sigmoid   32     0.4 0.3689394 0.17784843
## 129     linear   64     0.4 0.3318182 0.15475493
## 130 polynomial   64     0.4 0.3250000 0.09964033
## 131     radial   64     0.4 0.2984848 0.14712495
## 132    sigmoid   64     0.4 0.3924242 0.13047518
## 133     linear  128     0.4 0.3318182 0.15475493
## 134 polynomial  128     0.4 0.3250000 0.09964033
## 135     radial  128     0.4 0.3166667 0.14225938
## 136    sigmoid  128     0.4 0.3333333 0.11065102
## 137     linear  256     0.4 0.3318182 0.15475493
## 138 polynomial  256     0.4 0.3250000 0.09964033
## 139     radial  256     0.4 0.3166667 0.14225938
## 140    sigmoid  256     0.4 0.3954545 0.11609572
## 141     linear    4     0.5 0.3325758 0.15719367
## 142 polynomial    4     0.5 0.3068182 0.08035304
## 143     radial    4     0.5 0.2727273 0.13184624
## 144    sigmoid    4     0.5 0.3522727 0.14142541
## 145     linear    8     0.5 0.3318182 0.15475493
## 146 polynomial    8     0.5 0.3159091 0.09181993
## 147     radial    8     0.5 0.2977273 0.11736585
## 148    sigmoid    8     0.5 0.3772727 0.15569979
## 149     linear   16     0.5 0.3318182 0.15475493
## 150 polynomial   16     0.5 0.3068182 0.12768142
## 151     radial   16     0.5 0.3333333 0.11741745
## 152    sigmoid   16     0.5 0.3765152 0.14436186
## 153     linear   32     0.5 0.3318182 0.15475493
## 154 polynomial   32     0.5 0.3333333 0.12761898
## 155     radial   32     0.5 0.2984848 0.15419350
## 156    sigmoid   32     0.5 0.3689394 0.17784843
## 157     linear   64     0.5 0.3318182 0.15475493
## 158 polynomial   64     0.5 0.3250000 0.09964033
## 159     radial   64     0.5 0.2984848 0.14712495
## 160    sigmoid   64     0.5 0.3924242 0.13047518
## 161     linear  128     0.5 0.3318182 0.15475493
## 162 polynomial  128     0.5 0.3250000 0.09964033
## 163     radial  128     0.5 0.3166667 0.14225938
## 164    sigmoid  128     0.5 0.3333333 0.11065102
## 165     linear  256     0.5 0.3318182 0.15475493
## 166 polynomial  256     0.5 0.3250000 0.09964033
## 167     radial  256     0.5 0.3166667 0.14225938
## 168    sigmoid  256     0.5 0.3954545 0.11609572
## 169     linear    4     0.6 0.3325758 0.15719367
## 170 polynomial    4     0.6 0.3068182 0.08035304
## 171     radial    4     0.6 0.2727273 0.13184624
## 172    sigmoid    4     0.6 0.3522727 0.14142541
## 173     linear    8     0.6 0.3318182 0.15475493
## 174 polynomial    8     0.6 0.3159091 0.09181993
## 175     radial    8     0.6 0.2977273 0.11736585
## 176    sigmoid    8     0.6 0.3772727 0.15569979
## 177     linear   16     0.6 0.3318182 0.15475493
## 178 polynomial   16     0.6 0.3068182 0.12768142
## 179     radial   16     0.6 0.3333333 0.11741745
## 180    sigmoid   16     0.6 0.3765152 0.14436186
## 181     linear   32     0.6 0.3318182 0.15475493
## 182 polynomial   32     0.6 0.3333333 0.12761898
## 183     radial   32     0.6 0.2984848 0.15419350
## 184    sigmoid   32     0.6 0.3689394 0.17784843
## 185     linear   64     0.6 0.3318182 0.15475493
## 186 polynomial   64     0.6 0.3250000 0.09964033
## 187     radial   64     0.6 0.2984848 0.14712495
## 188    sigmoid   64     0.6 0.3924242 0.13047518
## 189     linear  128     0.6 0.3318182 0.15475493
## 190 polynomial  128     0.6 0.3250000 0.09964033
## 191     radial  128     0.6 0.3166667 0.14225938
## 192    sigmoid  128     0.6 0.3333333 0.11065102
## 193     linear  256     0.6 0.3318182 0.15475493
## 194 polynomial  256     0.6 0.3250000 0.09964033
## 195     radial  256     0.6 0.3166667 0.14225938
## 196    sigmoid  256     0.6 0.3954545 0.11609572
## 197     linear    4     0.7 0.3325758 0.15719367
## 198 polynomial    4     0.7 0.3068182 0.08035304
## 199     radial    4     0.7 0.2727273 0.13184624
## 200    sigmoid    4     0.7 0.3522727 0.14142541
## 201     linear    8     0.7 0.3318182 0.15475493
## 202 polynomial    8     0.7 0.3159091 0.09181993
## 203     radial    8     0.7 0.2977273 0.11736585
## 204    sigmoid    8     0.7 0.3772727 0.15569979
## 205     linear   16     0.7 0.3318182 0.15475493
## 206 polynomial   16     0.7 0.3068182 0.12768142
## 207     radial   16     0.7 0.3333333 0.11741745
## 208    sigmoid   16     0.7 0.3765152 0.14436186
## 209     linear   32     0.7 0.3318182 0.15475493
## 210 polynomial   32     0.7 0.3333333 0.12761898
## 211     radial   32     0.7 0.2984848 0.15419350
## 212    sigmoid   32     0.7 0.3689394 0.17784843
## 213     linear   64     0.7 0.3318182 0.15475493
## 214 polynomial   64     0.7 0.3250000 0.09964033
## 215     radial   64     0.7 0.2984848 0.14712495
## 216    sigmoid   64     0.7 0.3924242 0.13047518
## 217     linear  128     0.7 0.3318182 0.15475493
## 218 polynomial  128     0.7 0.3250000 0.09964033
## 219     radial  128     0.7 0.3166667 0.14225938
## 220    sigmoid  128     0.7 0.3333333 0.11065102
## 221     linear  256     0.7 0.3318182 0.15475493
## 222 polynomial  256     0.7 0.3250000 0.09964033
## 223     radial  256     0.7 0.3166667 0.14225938
## 224    sigmoid  256     0.7 0.3954545 0.11609572
## 225     linear    4     0.8 0.3325758 0.15719367
## 226 polynomial    4     0.8 0.3068182 0.08035304
## 227     radial    4     0.8 0.2727273 0.13184624
## 228    sigmoid    4     0.8 0.3522727 0.14142541
## 229     linear    8     0.8 0.3318182 0.15475493
## 230 polynomial    8     0.8 0.3159091 0.09181993
## 231     radial    8     0.8 0.2977273 0.11736585
## 232    sigmoid    8     0.8 0.3772727 0.15569979
## 233     linear   16     0.8 0.3318182 0.15475493
## 234 polynomial   16     0.8 0.3068182 0.12768142
## 235     radial   16     0.8 0.3333333 0.11741745
## 236    sigmoid   16     0.8 0.3765152 0.14436186
## 237     linear   32     0.8 0.3318182 0.15475493
## 238 polynomial   32     0.8 0.3333333 0.12761898
## 239     radial   32     0.8 0.2984848 0.15419350
## 240    sigmoid   32     0.8 0.3689394 0.17784843
## 241     linear   64     0.8 0.3318182 0.15475493
## 242 polynomial   64     0.8 0.3250000 0.09964033
## 243     radial   64     0.8 0.2984848 0.14712495
## 244    sigmoid   64     0.8 0.3924242 0.13047518
## 245     linear  128     0.8 0.3318182 0.15475493
## 246 polynomial  128     0.8 0.3250000 0.09964033
## 247     radial  128     0.8 0.3166667 0.14225938
## 248    sigmoid  128     0.8 0.3333333 0.11065102
## 249     linear  256     0.8 0.3318182 0.15475493
## 250 polynomial  256     0.8 0.3250000 0.09964033
## 251     radial  256     0.8 0.3166667 0.14225938
## 252    sigmoid  256     0.8 0.3954545 0.11609572
## 253     linear    4     0.9 0.3325758 0.15719367
## 254 polynomial    4     0.9 0.3068182 0.08035304
## 255     radial    4     0.9 0.2727273 0.13184624
## 256    sigmoid    4     0.9 0.3522727 0.14142541
## 257     linear    8     0.9 0.3318182 0.15475493
## 258 polynomial    8     0.9 0.3159091 0.09181993
## 259     radial    8     0.9 0.2977273 0.11736585
## 260    sigmoid    8     0.9 0.3772727 0.15569979
## 261     linear   16     0.9 0.3318182 0.15475493
## 262 polynomial   16     0.9 0.3068182 0.12768142
## 263     radial   16     0.9 0.3333333 0.11741745
## 264    sigmoid   16     0.9 0.3765152 0.14436186
## 265     linear   32     0.9 0.3318182 0.15475493
## 266 polynomial   32     0.9 0.3333333 0.12761898
## 267     radial   32     0.9 0.2984848 0.15419350
## 268    sigmoid   32     0.9 0.3689394 0.17784843
## 269     linear   64     0.9 0.3318182 0.15475493
## 270 polynomial   64     0.9 0.3250000 0.09964033
## 271     radial   64     0.9 0.2984848 0.14712495
## 272    sigmoid   64     0.9 0.3924242 0.13047518
## 273     linear  128     0.9 0.3318182 0.15475493
## 274 polynomial  128     0.9 0.3250000 0.09964033
## 275     radial  128     0.9 0.3166667 0.14225938
## 276    sigmoid  128     0.9 0.3333333 0.11065102
## 277     linear  256     0.9 0.3318182 0.15475493
## 278 polynomial  256     0.9 0.3250000 0.09964033
## 279     radial  256     0.9 0.3166667 0.14225938
## 280    sigmoid  256     0.9 0.3954545 0.11609572
## 281     linear    4     1.0 0.3325758 0.15719367
## 282 polynomial    4     1.0 0.3068182 0.08035304
## 283     radial    4     1.0 0.2727273 0.13184624
## 284    sigmoid    4     1.0 0.3522727 0.14142541
## 285     linear    8     1.0 0.3318182 0.15475493
## 286 polynomial    8     1.0 0.3159091 0.09181993
## 287     radial    8     1.0 0.2977273 0.11736585
## 288    sigmoid    8     1.0 0.3772727 0.15569979
## 289     linear   16     1.0 0.3318182 0.15475493
## 290 polynomial   16     1.0 0.3068182 0.12768142
## 291     radial   16     1.0 0.3333333 0.11741745
## 292    sigmoid   16     1.0 0.3765152 0.14436186
## 293     linear   32     1.0 0.3318182 0.15475493
## 294 polynomial   32     1.0 0.3333333 0.12761898
## 295     radial   32     1.0 0.2984848 0.15419350
## 296    sigmoid   32     1.0 0.3689394 0.17784843
## 297     linear   64     1.0 0.3318182 0.15475493
## 298 polynomial   64     1.0 0.3250000 0.09964033
## 299     radial   64     1.0 0.2984848 0.14712495
## 300    sigmoid   64     1.0 0.3924242 0.13047518
## 301     linear  128     1.0 0.3318182 0.15475493
## 302 polynomial  128     1.0 0.3250000 0.09964033
## 303     radial  128     1.0 0.3166667 0.14225938
## 304    sigmoid  128     1.0 0.3333333 0.11065102
## 305     linear  256     1.0 0.3318182 0.15475493
## 306 polynomial  256     1.0 0.3250000 0.09964033
## 307     radial  256     1.0 0.3166667 0.14225938
## 308    sigmoid  256     1.0 0.3954545 0.11609572
##           Reference
## Prediction  0  1
##          0 15  7
##          1  4  2
## [1] 0.6071429

SVM - Via PCA

## Importance of components:
##                           PC1    PC2     PC3     PC4     PC5     PC6     PC7
## Standard deviation     3.4453 2.2528 1.55906 1.37007 1.34635 1.28866 1.27728
## Proportion of Variance 0.2422 0.1036 0.04961 0.03831 0.03699 0.03389 0.03329
## Cumulative Proportion  0.2422 0.3458 0.39543 0.43374 0.47073 0.50462 0.53792
##                            PC8     PC9    PC10    PC11    PC12    PC13    PC14
## Standard deviation     1.22380 1.19497 1.16886 1.08459 1.05487 1.04846 1.03414
## Proportion of Variance 0.03057 0.02914 0.02788 0.02401 0.02271 0.02243 0.02183
## Cumulative Proportion  0.56848 0.59762 0.62551 0.64951 0.67222 0.69466 0.71648
##                           PC15    PC16    PC17    PC18    PC19   PC20   PC21
## Standard deviation     1.00751 0.96060 0.95823 0.91873 0.87611 0.8400 0.8340
## Proportion of Variance 0.02072 0.01883 0.01874 0.01723 0.01566 0.0144 0.0142
## Cumulative Proportion  0.73720 0.75603 0.77477 0.79199 0.80766 0.8221 0.8363
##                           PC22    PC23    PC24    PC25    PC26    PC27    PC28
## Standard deviation     0.81062 0.78359 0.76540 0.69345 0.67529 0.64590 0.63526
## Proportion of Variance 0.01341 0.01253 0.01196 0.00981 0.00931 0.00851 0.00824
## Cumulative Proportion  0.84966 0.86219 0.87415 0.88396 0.89327 0.90178 0.91002
##                           PC29    PC30    PC31    PC32    PC33    PC34    PC35
## Standard deviation     0.62827 0.60068 0.59058 0.57195 0.56669 0.54155 0.52992
## Proportion of Variance 0.00806 0.00736 0.00712 0.00668 0.00655 0.00599 0.00573
## Cumulative Proportion  0.91808 0.92544 0.93256 0.93923 0.94579 0.95177 0.95750
##                          PC36    PC37    PC38    PC39    PC40    PC41    PC42
## Standard deviation     0.5050 0.48908 0.47771 0.44431 0.42168 0.39567 0.37971
## Proportion of Variance 0.0052 0.00488 0.00466 0.00403 0.00363 0.00319 0.00294
## Cumulative Proportion  0.9627 0.96759 0.97225 0.97628 0.97990 0.98310 0.98604
##                           PC43    PC44    PC45    PC46   PC47    PC48    PC49
## Standard deviation     0.36480 0.34923 0.32573 0.31662 0.2968 0.27565 0.24184
## Proportion of Variance 0.00272 0.00249 0.00217 0.00205 0.0018 0.00155 0.00119
## Cumulative Proportion  0.98876 0.99125 0.99341 0.99546 0.9973 0.99881 1.00000

## 
## Parameter tuning of 'svm':
## 
## - sampling method: 10-fold cross validation 
## 
## - best parameters:
##   kernel cost epsilon
##  sigmoid    4       0
## 
## - best performance: 0.2628788 
## 
## - Detailed performance results:
##         kernel cost epsilon     error dispersion
## 1       linear    4     0.0 0.3318182 0.08396607
## 2   polynomial    4     0.0 0.2878788 0.09360481
## 3       radial    4     0.0 0.2825758 0.10406063
## 4      sigmoid    4     0.0 0.2628788 0.05680976
## 5       linear    8     0.0 0.3227273 0.07346706
## 6   polynomial    8     0.0 0.2962121 0.14924435
## 7       radial    8     0.0 0.2901515 0.13343517
## 8      sigmoid    8     0.0 0.2969697 0.14248333
## 9       linear   16     0.0 0.3227273 0.07346706
## 10  polynomial   16     0.0 0.3136364 0.13992725
## 11      radial   16     0.0 0.3159091 0.12821968
## 12     sigmoid   16     0.0 0.3242424 0.08890897
## 13      linear   32     0.0 0.3227273 0.07346706
## 14  polynomial   32     0.0 0.3393939 0.15586353
## 15      radial   32     0.0 0.3159091 0.12821968
## 16     sigmoid   32     0.0 0.3772727 0.09270119
## 17      linear   64     0.0 0.3227273 0.07346706
## 18  polynomial   64     0.0 0.3393939 0.15586353
## 19      radial   64     0.0 0.3159091 0.12821968
## 20     sigmoid   64     0.0 0.3568182 0.10881758
## 21      linear  128     0.0 0.3227273 0.07346706
## 22  polynomial  128     0.0 0.3393939 0.15586353
## 23      radial  128     0.0 0.3159091 0.12821968
## 24     sigmoid  128     0.0 0.3659091 0.13533328
## 25      linear  256     0.0 0.3227273 0.07346706
## 26  polynomial  256     0.0 0.3393939 0.15586353
## 27      radial  256     0.0 0.3159091 0.12821968
## 28     sigmoid  256     0.0 0.3431818 0.11638374
## 29      linear    4     0.1 0.3318182 0.08396607
## 30  polynomial    4     0.1 0.2878788 0.09360481
## 31      radial    4     0.1 0.2825758 0.10406063
## 32     sigmoid    4     0.1 0.2628788 0.05680976
## 33      linear    8     0.1 0.3227273 0.07346706
## 34  polynomial    8     0.1 0.2962121 0.14924435
## 35      radial    8     0.1 0.2901515 0.13343517
## 36     sigmoid    8     0.1 0.2969697 0.14248333
## 37      linear   16     0.1 0.3227273 0.07346706
## 38  polynomial   16     0.1 0.3136364 0.13992725
## 39      radial   16     0.1 0.3159091 0.12821968
## 40     sigmoid   16     0.1 0.3242424 0.08890897
## 41      linear   32     0.1 0.3227273 0.07346706
## 42  polynomial   32     0.1 0.3393939 0.15586353
## 43      radial   32     0.1 0.3159091 0.12821968
## 44     sigmoid   32     0.1 0.3772727 0.09270119
## 45      linear   64     0.1 0.3227273 0.07346706
## 46  polynomial   64     0.1 0.3393939 0.15586353
## 47      radial   64     0.1 0.3159091 0.12821968
## 48     sigmoid   64     0.1 0.3568182 0.10881758
## 49      linear  128     0.1 0.3227273 0.07346706
## 50  polynomial  128     0.1 0.3393939 0.15586353
## 51      radial  128     0.1 0.3159091 0.12821968
## 52     sigmoid  128     0.1 0.3659091 0.13533328
## 53      linear  256     0.1 0.3227273 0.07346706
## 54  polynomial  256     0.1 0.3393939 0.15586353
## 55      radial  256     0.1 0.3159091 0.12821968
## 56     sigmoid  256     0.1 0.3431818 0.11638374
## 57      linear    4     0.2 0.3318182 0.08396607
## 58  polynomial    4     0.2 0.2878788 0.09360481
## 59      radial    4     0.2 0.2825758 0.10406063
## 60     sigmoid    4     0.2 0.2628788 0.05680976
## 61      linear    8     0.2 0.3227273 0.07346706
## 62  polynomial    8     0.2 0.2962121 0.14924435
## 63      radial    8     0.2 0.2901515 0.13343517
## 64     sigmoid    8     0.2 0.2969697 0.14248333
## 65      linear   16     0.2 0.3227273 0.07346706
## 66  polynomial   16     0.2 0.3136364 0.13992725
## 67      radial   16     0.2 0.3159091 0.12821968
## 68     sigmoid   16     0.2 0.3242424 0.08890897
## 69      linear   32     0.2 0.3227273 0.07346706
## 70  polynomial   32     0.2 0.3393939 0.15586353
## 71      radial   32     0.2 0.3159091 0.12821968
## 72     sigmoid   32     0.2 0.3772727 0.09270119
## 73      linear   64     0.2 0.3227273 0.07346706
## 74  polynomial   64     0.2 0.3393939 0.15586353
## 75      radial   64     0.2 0.3159091 0.12821968
## 76     sigmoid   64     0.2 0.3568182 0.10881758
## 77      linear  128     0.2 0.3227273 0.07346706
## 78  polynomial  128     0.2 0.3393939 0.15586353
## 79      radial  128     0.2 0.3159091 0.12821968
## 80     sigmoid  128     0.2 0.3659091 0.13533328
## 81      linear  256     0.2 0.3227273 0.07346706
## 82  polynomial  256     0.2 0.3393939 0.15586353
## 83      radial  256     0.2 0.3159091 0.12821968
## 84     sigmoid  256     0.2 0.3431818 0.11638374
## 85      linear    4     0.3 0.3318182 0.08396607
## 86  polynomial    4     0.3 0.2878788 0.09360481
## 87      radial    4     0.3 0.2825758 0.10406063
## 88     sigmoid    4     0.3 0.2628788 0.05680976
## 89      linear    8     0.3 0.3227273 0.07346706
## 90  polynomial    8     0.3 0.2962121 0.14924435
## 91      radial    8     0.3 0.2901515 0.13343517
## 92     sigmoid    8     0.3 0.2969697 0.14248333
## 93      linear   16     0.3 0.3227273 0.07346706
## 94  polynomial   16     0.3 0.3136364 0.13992725
## 95      radial   16     0.3 0.3159091 0.12821968
## 96     sigmoid   16     0.3 0.3242424 0.08890897
## 97      linear   32     0.3 0.3227273 0.07346706
## 98  polynomial   32     0.3 0.3393939 0.15586353
## 99      radial   32     0.3 0.3159091 0.12821968
## 100    sigmoid   32     0.3 0.3772727 0.09270119
## 101     linear   64     0.3 0.3227273 0.07346706
## 102 polynomial   64     0.3 0.3393939 0.15586353
## 103     radial   64     0.3 0.3159091 0.12821968
## 104    sigmoid   64     0.3 0.3568182 0.10881758
## 105     linear  128     0.3 0.3227273 0.07346706
## 106 polynomial  128     0.3 0.3393939 0.15586353
## 107     radial  128     0.3 0.3159091 0.12821968
## 108    sigmoid  128     0.3 0.3659091 0.13533328
## 109     linear  256     0.3 0.3227273 0.07346706
## 110 polynomial  256     0.3 0.3393939 0.15586353
## 111     radial  256     0.3 0.3159091 0.12821968
## 112    sigmoid  256     0.3 0.3431818 0.11638374
## 113     linear    4     0.4 0.3318182 0.08396607
## 114 polynomial    4     0.4 0.2878788 0.09360481
## 115     radial    4     0.4 0.2825758 0.10406063
## 116    sigmoid    4     0.4 0.2628788 0.05680976
## 117     linear    8     0.4 0.3227273 0.07346706
## 118 polynomial    8     0.4 0.2962121 0.14924435
## 119     radial    8     0.4 0.2901515 0.13343517
## 120    sigmoid    8     0.4 0.2969697 0.14248333
## 121     linear   16     0.4 0.3227273 0.07346706
## 122 polynomial   16     0.4 0.3136364 0.13992725
## 123     radial   16     0.4 0.3159091 0.12821968
## 124    sigmoid   16     0.4 0.3242424 0.08890897
## 125     linear   32     0.4 0.3227273 0.07346706
## 126 polynomial   32     0.4 0.3393939 0.15586353
## 127     radial   32     0.4 0.3159091 0.12821968
## 128    sigmoid   32     0.4 0.3772727 0.09270119
## 129     linear   64     0.4 0.3227273 0.07346706
## 130 polynomial   64     0.4 0.3393939 0.15586353
## 131     radial   64     0.4 0.3159091 0.12821968
## 132    sigmoid   64     0.4 0.3568182 0.10881758
## 133     linear  128     0.4 0.3227273 0.07346706
## 134 polynomial  128     0.4 0.3393939 0.15586353
## 135     radial  128     0.4 0.3159091 0.12821968
## 136    sigmoid  128     0.4 0.3659091 0.13533328
## 137     linear  256     0.4 0.3227273 0.07346706
## 138 polynomial  256     0.4 0.3393939 0.15586353
## 139     radial  256     0.4 0.3159091 0.12821968
## 140    sigmoid  256     0.4 0.3431818 0.11638374
## 141     linear    4     0.5 0.3318182 0.08396607
## 142 polynomial    4     0.5 0.2878788 0.09360481
## 143     radial    4     0.5 0.2825758 0.10406063
## 144    sigmoid    4     0.5 0.2628788 0.05680976
## 145     linear    8     0.5 0.3227273 0.07346706
## 146 polynomial    8     0.5 0.2962121 0.14924435
## 147     radial    8     0.5 0.2901515 0.13343517
## 148    sigmoid    8     0.5 0.2969697 0.14248333
## 149     linear   16     0.5 0.3227273 0.07346706
## 150 polynomial   16     0.5 0.3136364 0.13992725
## 151     radial   16     0.5 0.3159091 0.12821968
## 152    sigmoid   16     0.5 0.3242424 0.08890897
## 153     linear   32     0.5 0.3227273 0.07346706
## 154 polynomial   32     0.5 0.3393939 0.15586353
## 155     radial   32     0.5 0.3159091 0.12821968
## 156    sigmoid   32     0.5 0.3772727 0.09270119
## 157     linear   64     0.5 0.3227273 0.07346706
## 158 polynomial   64     0.5 0.3393939 0.15586353
## 159     radial   64     0.5 0.3159091 0.12821968
## 160    sigmoid   64     0.5 0.3568182 0.10881758
## 161     linear  128     0.5 0.3227273 0.07346706
## 162 polynomial  128     0.5 0.3393939 0.15586353
## 163     radial  128     0.5 0.3159091 0.12821968
## 164    sigmoid  128     0.5 0.3659091 0.13533328
## 165     linear  256     0.5 0.3227273 0.07346706
## 166 polynomial  256     0.5 0.3393939 0.15586353
## 167     radial  256     0.5 0.3159091 0.12821968
## 168    sigmoid  256     0.5 0.3431818 0.11638374
## 169     linear    4     0.6 0.3318182 0.08396607
## 170 polynomial    4     0.6 0.2878788 0.09360481
## 171     radial    4     0.6 0.2825758 0.10406063
## 172    sigmoid    4     0.6 0.2628788 0.05680976
## 173     linear    8     0.6 0.3227273 0.07346706
## 174 polynomial    8     0.6 0.2962121 0.14924435
## 175     radial    8     0.6 0.2901515 0.13343517
## 176    sigmoid    8     0.6 0.2969697 0.14248333
## 177     linear   16     0.6 0.3227273 0.07346706
## 178 polynomial   16     0.6 0.3136364 0.13992725
## 179     radial   16     0.6 0.3159091 0.12821968
## 180    sigmoid   16     0.6 0.3242424 0.08890897
## 181     linear   32     0.6 0.3227273 0.07346706
## 182 polynomial   32     0.6 0.3393939 0.15586353
## 183     radial   32     0.6 0.3159091 0.12821968
## 184    sigmoid   32     0.6 0.3772727 0.09270119
## 185     linear   64     0.6 0.3227273 0.07346706
## 186 polynomial   64     0.6 0.3393939 0.15586353
## 187     radial   64     0.6 0.3159091 0.12821968
## 188    sigmoid   64     0.6 0.3568182 0.10881758
## 189     linear  128     0.6 0.3227273 0.07346706
## 190 polynomial  128     0.6 0.3393939 0.15586353
## 191     radial  128     0.6 0.3159091 0.12821968
## 192    sigmoid  128     0.6 0.3659091 0.13533328
## 193     linear  256     0.6 0.3227273 0.07346706
## 194 polynomial  256     0.6 0.3393939 0.15586353
## 195     radial  256     0.6 0.3159091 0.12821968
## 196    sigmoid  256     0.6 0.3431818 0.11638374
## 197     linear    4     0.7 0.3318182 0.08396607
## 198 polynomial    4     0.7 0.2878788 0.09360481
## 199     radial    4     0.7 0.2825758 0.10406063
## 200    sigmoid    4     0.7 0.2628788 0.05680976
## 201     linear    8     0.7 0.3227273 0.07346706
## 202 polynomial    8     0.7 0.2962121 0.14924435
## 203     radial    8     0.7 0.2901515 0.13343517
## 204    sigmoid    8     0.7 0.2969697 0.14248333
## 205     linear   16     0.7 0.3227273 0.07346706
## 206 polynomial   16     0.7 0.3136364 0.13992725
## 207     radial   16     0.7 0.3159091 0.12821968
## 208    sigmoid   16     0.7 0.3242424 0.08890897
## 209     linear   32     0.7 0.3227273 0.07346706
## 210 polynomial   32     0.7 0.3393939 0.15586353
## 211     radial   32     0.7 0.3159091 0.12821968
## 212    sigmoid   32     0.7 0.3772727 0.09270119
## 213     linear   64     0.7 0.3227273 0.07346706
## 214 polynomial   64     0.7 0.3393939 0.15586353
## 215     radial   64     0.7 0.3159091 0.12821968
## 216    sigmoid   64     0.7 0.3568182 0.10881758
## 217     linear  128     0.7 0.3227273 0.07346706
## 218 polynomial  128     0.7 0.3393939 0.15586353
## 219     radial  128     0.7 0.3159091 0.12821968
## 220    sigmoid  128     0.7 0.3659091 0.13533328
## 221     linear  256     0.7 0.3227273 0.07346706
## 222 polynomial  256     0.7 0.3393939 0.15586353
## 223     radial  256     0.7 0.3159091 0.12821968
## 224    sigmoid  256     0.7 0.3431818 0.11638374
## 225     linear    4     0.8 0.3318182 0.08396607
## 226 polynomial    4     0.8 0.2878788 0.09360481
## 227     radial    4     0.8 0.2825758 0.10406063
## 228    sigmoid    4     0.8 0.2628788 0.05680976
## 229     linear    8     0.8 0.3227273 0.07346706
## 230 polynomial    8     0.8 0.2962121 0.14924435
## 231     radial    8     0.8 0.2901515 0.13343517
## 232    sigmoid    8     0.8 0.2969697 0.14248333
## 233     linear   16     0.8 0.3227273 0.07346706
## 234 polynomial   16     0.8 0.3136364 0.13992725
## 235     radial   16     0.8 0.3159091 0.12821968
## 236    sigmoid   16     0.8 0.3242424 0.08890897
## 237     linear   32     0.8 0.3227273 0.07346706
## 238 polynomial   32     0.8 0.3393939 0.15586353
## 239     radial   32     0.8 0.3159091 0.12821968
## 240    sigmoid   32     0.8 0.3772727 0.09270119
## 241     linear   64     0.8 0.3227273 0.07346706
## 242 polynomial   64     0.8 0.3393939 0.15586353
## 243     radial   64     0.8 0.3159091 0.12821968
## 244    sigmoid   64     0.8 0.3568182 0.10881758
## 245     linear  128     0.8 0.3227273 0.07346706
## 246 polynomial  128     0.8 0.3393939 0.15586353
## 247     radial  128     0.8 0.3159091 0.12821968
## 248    sigmoid  128     0.8 0.3659091 0.13533328
## 249     linear  256     0.8 0.3227273 0.07346706
## 250 polynomial  256     0.8 0.3393939 0.15586353
## 251     radial  256     0.8 0.3159091 0.12821968
## 252    sigmoid  256     0.8 0.3431818 0.11638374
## 253     linear    4     0.9 0.3318182 0.08396607
## 254 polynomial    4     0.9 0.2878788 0.09360481
## 255     radial    4     0.9 0.2825758 0.10406063
## 256    sigmoid    4     0.9 0.2628788 0.05680976
## 257     linear    8     0.9 0.3227273 0.07346706
## 258 polynomial    8     0.9 0.2962121 0.14924435
## 259     radial    8     0.9 0.2901515 0.13343517
## 260    sigmoid    8     0.9 0.2969697 0.14248333
## 261     linear   16     0.9 0.3227273 0.07346706
## 262 polynomial   16     0.9 0.3136364 0.13992725
## 263     radial   16     0.9 0.3159091 0.12821968
## 264    sigmoid   16     0.9 0.3242424 0.08890897
## 265     linear   32     0.9 0.3227273 0.07346706
## 266 polynomial   32     0.9 0.3393939 0.15586353
## 267     radial   32     0.9 0.3159091 0.12821968
## 268    sigmoid   32     0.9 0.3772727 0.09270119
## 269     linear   64     0.9 0.3227273 0.07346706
## 270 polynomial   64     0.9 0.3393939 0.15586353
## 271     radial   64     0.9 0.3159091 0.12821968
## 272    sigmoid   64     0.9 0.3568182 0.10881758
## 273     linear  128     0.9 0.3227273 0.07346706
## 274 polynomial  128     0.9 0.3393939 0.15586353
## 275     radial  128     0.9 0.3159091 0.12821968
## 276    sigmoid  128     0.9 0.3659091 0.13533328
## 277     linear  256     0.9 0.3227273 0.07346706
## 278 polynomial  256     0.9 0.3393939 0.15586353
## 279     radial  256     0.9 0.3159091 0.12821968
## 280    sigmoid  256     0.9 0.3431818 0.11638374
## 281     linear    4     1.0 0.3318182 0.08396607
## 282 polynomial    4     1.0 0.2878788 0.09360481
## 283     radial    4     1.0 0.2825758 0.10406063
## 284    sigmoid    4     1.0 0.2628788 0.05680976
## 285     linear    8     1.0 0.3227273 0.07346706
## 286 polynomial    8     1.0 0.2962121 0.14924435
## 287     radial    8     1.0 0.2901515 0.13343517
## 288    sigmoid    8     1.0 0.2969697 0.14248333
## 289     linear   16     1.0 0.3227273 0.07346706
## 290 polynomial   16     1.0 0.3136364 0.13992725
## 291     radial   16     1.0 0.3159091 0.12821968
## 292    sigmoid   16     1.0 0.3242424 0.08890897
## 293     linear   32     1.0 0.3227273 0.07346706
## 294 polynomial   32     1.0 0.3393939 0.15586353
## 295     radial   32     1.0 0.3159091 0.12821968
## 296    sigmoid   32     1.0 0.3772727 0.09270119
## 297     linear   64     1.0 0.3227273 0.07346706
## 298 polynomial   64     1.0 0.3393939 0.15586353
## 299     radial   64     1.0 0.3159091 0.12821968
## 300    sigmoid   64     1.0 0.3568182 0.10881758
## 301     linear  128     1.0 0.3227273 0.07346706
## 302 polynomial  128     1.0 0.3393939 0.15586353
## 303     radial  128     1.0 0.3159091 0.12821968
## 304    sigmoid  128     1.0 0.3659091 0.13533328
## 305     linear  256     1.0 0.3227273 0.07346706
## 306 polynomial  256     1.0 0.3393939 0.15586353
## 307     radial  256     1.0 0.3159091 0.12821968
## 308    sigmoid  256     1.0 0.3431818 0.11638374
##           Reference
## Prediction  0  1
##          0 14  7
##          1  5  2
## [1] 0.5714286